A familiar voice for imagined worlds and everyday moments.
JoyFox-Reverie-27B is a conversational model from JoyFox, derived from Qwen3.8-27B and focused on character roleplay, shared storytelling, and companion dialogue.
“Reverie” evokes the space between imagination and conversation: a character to meet, a story to explore, or a familiar voice to talk to at the end of the day. Define the experience through your prompt, from a richly described fictional setting to a relaxed everyday exchange.
✨ Highlights
- Character dialogue: shape a persona through background, voice, relationships, and scene instructions.
- Companion conversations: set a friendly conversational style for everyday topics and personal reflection.
- Shared storytelling: create dialogue and scenes together, defining which characters each participant controls.
- Flexible expression: specify the reply language, narrative perspective, response length, and balance of action and dialogue.
- Thinking controls: choose direct replies or enable reasoning through the supplied chat template.
- Visual context: the checkpoint retains the upstream visual components for image and video inputs through compatible processors and runtimes.
🚀 Quickstart
Transformers
Use a current Transformers build that supports the Qwen3.8 checkpoint and its processor. Architecture loading details are covered in the Transformers documentation.
pip install -U torch transformers accelerate
The following example starts a character conversation with thinking disabled:
import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
model_id = "joyfox/JoyFox-Reverie-27B"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
model_id,
dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are Mira, an old friend who runs a seaside bookshop. Speak naturally, stay in character, and let me choose my own actions."},
{"role": "user", "content": "I push open the shop door after a long day. Got a moment to talk?"},
]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
enable_thinking=False,
return_dict=True,
return_tensors="pt",
).to(model.device)
with torch.inference_mode():
output = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9,
top_k=20,
)
reply = output[0, inputs["input_ids"].shape[-1]:]
print(processor.decode(reply, skip_special_tokens=True))
Serving with vLLM
The following command exposes a chat endpoint with a 32K context setting. Allocate enough GPU memory for the BF16 weights and runtime state; increase tensor parallelism when using multiple GPUs.
pip install -U vllm
vllm serve joyfox/JoyFox-Reverie-27B \
--served-model-name JoyFox-Reverie-27B \
--dtype bfloat16 \
--max-model-len 32768 \
--reasoning-parser qwen3
For multiple GPUs, add --tensor-parallel-size with your GPU count. Context capacity and concurrency depend on available memory. Consult the vLLM Qwen3.8 recipe for framework requirements and hardware configuration.
Chat API
Once the server is running, send a conversation request:
curl http://localhost:8000/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{
"model": "JoyFox-Reverie-27B",
"messages": [
{
"role": "system",
"content": "Speak naturally as my old friend Mira. Let me choose my own actions."
},
{
"role": "user",
"content": "I had a long day. Can we talk for a bit?"
}
],
"temperature": 0.7,
"top_p": 0.9,
"max_tokens": 512,
"chat_template_kwargs": {
"enable_thinking": false
}
}'
For multi-turn chat, append each assistant reply and the next user message to messages before sending the next request. The server uses the model's supplied chat template.
Image Input
With the model and processor from the Transformers example, replace messages with the following, then run the same template and generation steps:
messages = [
{"role": "system", "content": "Describe the scene as Mira, a seaside bookshop owner. Distinguish what you see from what you imagine."},
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
{"type": "text", "text": "What do you notice in this picture?"},
],
},
]
Image and video processing depends on the serving framework. For video input formats and media controls, refer to the upstream usage guide.
💬 Conversation Styles
Character Roleplay
A character prompt works best when it identifies the speaker, the relationship, and the current scene:
You are Mira, the owner of a small seaside bookshop and an old friend of mine.
Voice: warm, observant, with occasional dry humor.
Scene: a rainy evening just before closing time.
Write mostly dialogue, with brief actions where useful.
Keep the setting grounded in established facts. Leave my actions and decisions to me.
Companion Chat
For a conversation without a fictional scene:
Talk with me like a thoughtful friend. Use natural, relaxed language and follow the topic I bring up. Ask questions when they help, and share your perspective without turning every reply into advice. Avoid stage directions unless I ask for roleplay.
Shared Storytelling
We are writing a mystery together. You control the supporting cast and environment; I control the protagonist. Build on established events, introduce clues gradually, and leave room for my next decision.
🧠 Thinking Controls
For direct character dialogue, pass enable_thinking=False to the chat template. Thinking is enabled by default in the included template.
When reasoning is useful, these options can be passed to processor.apply_chat_template:
chat_options = {
"enable_thinking": True,
"reasoning_effort": "medium",
"preserve_thinking": True,
}
reasoning_effort accepts low, medium, and xhigh (the default). preserve_thinking controls retention of reasoning content in earlier assistant messages. For the chat API, place template options inside chat_template_kwargs; the server's reasoning parser separates reasoning from the answer.
🎛️ Generation Settings
The quickstart uses temperature 0.7, top-p 0.9, and top-k 20 for direct dialogue. The included generation configuration uses temperature 1.0, top-p 0.95, and top-k 20.
Treat these as starting points. Lower temperature for more restrained replies; increase it when exploring more varied phrasing. Adjust one setting at a time, and keep enough output tokens for the desired response length. Sampling options vary across serving frameworks.
💡 Best Practices
- Keep the character card concise and explicit. Separate permanent identity from the current scene.
- Carry earlier messages forward so the model can use established events and relationships.
- State who controls each character and whether the model may introduce new events.
- Specify the language, point of view, and desired length rather than relying on implied style.
- For long sessions, maintain a short summary of established facts alongside recent messages.
- Treat fictional dialogue as generated fiction, and verify factual statements when accuracy matters.
📦 GGUF
UD quantizations from Q4 through Q6 are available in GGUF. Every main file includes MTP tensors; use the matching F16 mmproj for images.
Download a GGUF
Use a current llama.cpp build with qwen35 support. Download one main GGUF; the matching mmproj is needed only for image input. The examples below use Q4 XL; replace the main filename to use another available quantization.
pip install -U huggingface_hub
hf download joyfox/JoyFox-Reverie-27B \
GGUF/JoyFox-Reverie-27B-UD-Q4_K_XL.gguf \
GGUF/mmproj-JoyFox-Reverie-27B-F16.gguf \
--local-dir JoyFox-Reverie-27B
cd JoyFox-Reverie-27B
Text Chat
Run the following command from the downloaded model directory:
llama-cli -m GGUF/JoyFox-Reverie-27B-UD-Q4_K_XL.gguf \
--jinja --reasoning off -c 32768 -ngl all \
--temp 0.7 --top-p 0.9 --top-k 20
--jinja applies the bundled chat template; --reasoning off selects direct replies. Adjust -c for your desired context and available memory. -ngl all offloads the model to the GPU; reduce the offload when it does not fit. The download size excludes runtime buffers and KV cache.
Chat Server
llama-server -m GGUF/JoyFox-Reverie-27B-UD-Q4_K_XL.gguf \
--jinja --reasoning off -c 32768 -ngl all \
--host 127.0.0.1 --port 8080
The server provides a web interface at http://127.0.0.1:8080 and a chat endpoint at /v1/chat/completions:
curl http://127.0.0.1:8080/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{"messages":[{"role":"system","content":"You are Mira, my old friend. Speak naturally."},{"role":"user","content":"How was your day?"}],"temperature":0.7,"top_p":0.9,"max_tokens":256}'
Image Input
Use the matching F16 visual projector with your chosen main GGUF:
llama-cli -m GGUF/JoyFox-Reverie-27B-UD-Q4_K_XL.gguf \
--mmproj GGUF/mmproj-JoyFox-Reverie-27B-F16.gguf \
--image example.png --prompt "Describe this image." \
--jinja --reasoning off -c 4096 -ngl all
Optional MTP Decoding
All main GGUF files include native MTP tensors. On a compatible llama.cpp build, start the server with bundled MTP:
llama-server -m GGUF/JoyFox-Reverie-27B-UD-Q4_K_XL.gguf \
--jinja --reasoning off -c 32768 -ngl all \
--spec-type draft-mtp --spec-draft-n-max 3 \
--host 127.0.0.1 --port 8080
This replaces the ordinary server command. No separate draft-model download is required. MTP uses additional memory; speed depends on draft acceptance and the workload.
Choosing a Quantization
Q4 S has the smallest footprint in this release. Q4 XL offers a practical starting point for roleplay; Q5 and Q6 require more memory. Choose according to available memory and your preferred output quality. For a GGUF application, load the downloaded main file and use the supplied chat template; image input additionally requires the matching visual projector and application support.
📜 License and Acknowledgments
JoyFox-Reverie-27B is released under Apache-2.0. It is a JoyFox derivative of Qwen3.8-27B. Thanks to the Qwen team and the Hugging Face ecosystem.
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